AGNEP: An Agglomerative Nesting Clustering Algorithm for Phenotypic Dimension Reduction in Joint Analysis of Multiple
Fengrong Liu1,2, Ziyang Zhou1, Mingzhi Cai1
1College of Science, Nanjing Agricultural University, Nanjing, China.
Frontiers in Genetics
|May 13, 2021
Summary
We developed AGNEP, a novel method for joint analysis of multiple phenotypes in genome-wide association studies (GWAS). AGNEP improves statistical power and efficiency by clustering phenotypes before analysis, outperforming existing methods.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) identify genetic variants for complex traits.
- Joint analysis of multiple phenotypes can increase statistical power but existing methods often overlook phenotype correlations.
- Ignoring phenotypic correlations can reduce statistical power in GWAS.
Purpose of the Study:
- To develop a novel method, AGNEP, for dimension reduction in multi-phenotype GWAS.
- To improve statistical power and efficiency in identifying genetic variants associated with multiple traits.
- To better capture the genetic structure of correlated phenotypes.
Main Methods:
- AGNEP employs agglomerative nesting clustering to group correlated phenotypes.
- Principal Component Analysis (PCA) generates representative phenotypes for each cluster.
- Multivariate analysis tests associations between genetic variants and representative phenotypes.
Main Results:
- AGNEP demonstrated superior statistical power compared to established methods in simulations.
- The method showed improved computational efficiency and identified more quantitative trait nucleotides (QTNs).
- Analysis of 19 Arabidopsis phenotypes confirmed AGNEP's efficiency in QTN detection.
Conclusions:
- AGNEP offers a powerful and efficient approach for multi-phenotype GWAS.
- The method effectively reduces dimensionality while preserving genetic structure.
- AGNEP enhances the discovery of genetic variants underlying complex traits.
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